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Purpose – It is globally recognized that food waste has significant economic, social and environmental impacts. This study endeavors to identify the individuals' underlying factors that affect food waste behavior in a social context in Egypt. Design/methodology/approach – Due to the scarcity of research illustrating food waste in a social context, a qualitative research paradigm is employed. In-depth semi-structured interviews are conducted with 18 Egyptian households to report their experiences, thoughts and feelings while eating in a social context. Findings – Based on thematic analysis using grounded theory procedures, eight main elements affecting creation and reduction of food waste in social contexts are identified. Additionally, the authors investigate the role of emotions in social dining contexts. A key finding is that food waste in social events is inevitable and is the acceptable norm in the Egyptian culture. Thus, the first step to reduce food waste is paying more attention to change culture. Persuading people with the negative consequences of food waste issue on behalf of their cultural values will be more challenging and may require more innovative approaches. Originality/value – Although food waste issue is one of the contemporary issues in the world, very little is known about how social food consumption leads to food waste behavior. This study employs social influence theory and the collectivistic culture orientation to investigate the unanswered question of why food gets wasted in social meal context.

Abstract Progranulin (PGRN) plays a critical role in bronchial asthma and the function of various immune cells. However, the mechanisms by which PGRN influences B‐cell receptor (BCR) signaling and immunoglobulin E(IgE) production are not fully understood. The study aimed to elucidate the molecular mechanisms through which PGRN affects BCR signaling, B‐cell differentiation, and IgE production. A PGRN knockout mouse model, along with techniques including flow cytometry, the creation of a bone marrow chimeric mouse model, total internal reflection fluorescence (TIRF), and Western blot (WB) analysis is employed, to investigate the link between PGRN and various aspects of B‐cell biology. It is discovered that the absence of PGRN in mice alters peripheral B‐cell subpopulations, promotes IgE class switching in a cell‐intrinsic manner, and affects B‐cell subpopulations. Additionally, PGRN modulates B‐cell functions by regulating BCR signaling pathways, metabolic processes, and the actin cytoskeleton during early B‐cell activation. Significantly, PGRN deficiency results in diminished production of NP‐specific antibodies. Moreover, it is found that PGRN inhibits B‐cell activation and IgE production through the PGRN‐IFITM3‐STAT1 signaling pathway. The findings provide new strategies for the targeted treatment of bronchial asthma, highlighting the crucial role of PGRN in B‐cell signaling and IgE production.

The global incidence and prevalence of inflammatory bowel disease (IBD) are gradually increasing. A high-fat diet (HFD) is known to disrupt intestinal homeostasis and aggravate IBD, yet the underlying mechanisms remain largely undefined. Here, a positive correlation between dietary fat intake and disease severity in both IBD patients and murine colitis models is observed. A HFD induces a significant decrease in indole-3-acetic acid (IAA) and leads to intestinal barrier damage. Furthermore, IAA supplementation enhances intestinal mucin sulfation and effectively alleviates colitis. Mechanistically, IAA upregulates key molecules involved in mucin sulfation, including 3’-phosphoadenosine 5’-phosphosulfate synthase 2 (Papss2) and solute carrier family 35 member B3 (Slc35b3), the synthesis enzyme and the transferase of 3’-phosphoadenosine-5’-phosphosulfate (PAPS), via the aryl hydrocarbon receptor (AHR). More importantly, AHR can directly bind to the transcription start site of Papss2. Oral administration of Lactobacillus reuteri, which can produce IAA, contributes to protecting against colitis and promoting mucin sulfation, while the modified L. reuteri strain lacking the iaaM gene (LactobacillusΔiaaM) and the ability to produce IAA fail to exhibit such effects. Overall, IAA enhances intestinal mucin sulfation through the AHR-Papss2-Slc35b3 pathway, contributing to the protection of intestinal homfeostasis.

BackgroundBetween 2020 and 2022, the COVID-19 pandemic spread globally, and the implementation of preventive measures led to reduced outdoor activities for older adults, resulting in a decline in social functioning. This study aims to improve community-based health interventions tailored to older adults experiencing physical and psychological declines following the COVID-19 pandemic.MethodsThis study utilized previous data mining results to estimate the demand for community health services for older adults. It involves collecting questionnaire responses to understand the basic characteristics and lifestyle behaviors of older adults. The severity of health issues among older adults was assessed using the KCL and GHQ-12 scales. Statistical analyses included descriptive statistics, chi-square tests, t-tests, ANOVA, non-parametric tests as applicable, and stratified binary logistic regression to determine the factors influencing the health status of older adults.ResultsOver 60% of the older adult population suffers from chronic diseases, and more than 70% do not participate in social activities. In the overall older adults, the detection rate for poor health is 15.60%. Chronic illness, reduced ability to perform daily activities, anxiety, poor self-rated health, sleep disturbances, and nutritional imbalance were identified as key risk factors affecting the health of older adults in the community.ConclusionOlder adults mainly engage in physical exercise, maintain a healthy lifestyle, and control their diet as self-care strategies. Early signs of frailty are characterized by declines in lower limb muscle function and memory. The most common manifestation of poor health among older adults is anxiety.

ABSTRACT Background The cumulative, health system‐wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real‐world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system‐wide survival impact of ICIs. Methods We identified all patients receiving PD‐1/PD‐L1 or CTLA‐4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non‐ICI systemic therapy in the years before ICI approval (historical control). ICI and historical control cohorts were matched on multiple cancer‐related prognostic factors, comorbidities, and demographics. The effect of ICI on overall survival was quantified with Cox regression incorporating matching weights. Cumulative life‐years gained system‐wide were calculated from the difference in adjusted 5‐year restricted mean survival times. Results There were 27,322 patients in the ICI cohort and 69,801 patients in the historical control cohort. Among ICI patients, the most common cancer types were NSCLC (46%) and melanoma (10%). ICI demonstrated a large OS benefit in most cancer types with heterogeneity across cancer types (NSCLC: adjusted HR [aHR] 0.56, 95% confidence interval [CI] 0.54–0.58, p

Abstract Background Patients with extensive‐stage small cell lung cancer (ES‐SCLC) have an exceptionally poor prognosis and immune checkpoint inhibitors (ICIs) combined with etoposide‐platinum is recommended as standard first‐line therapy. However, which combination pattern is the best still remains unknown. This network meta‐analysis was performed to compare the efficacy and safety of currently available patterns including an antiangiogenic agent containing regimen and probed into the most appropriate therapy for patients. Methods Hazard ratios (HRs) and odds ratios (ORs) were generated using R software. The outcomes of overall survival (OS), progression‐free survival (PFS), objective response rate (ORR), and adverse events of grade 3 or higher (grade ≥ 3 adverse events [AEs]) were analyzed. Results A total of 10 randomized controlled trials (RCTs) involving 5544 patients were included for analysis. Drug combination patterns included adebrelimab, atezolizumab, durvalumab, durvalumab plus tremelimumab, ipilimumab, pembrolizumab, serplulimab, benmelstobart plus anlotinib, tislelizumab, tiragolumab plus atezolizumab and toripalimab in combination with chemotherapy. The novel antiangiogenic agent containing regimen benmelstobart + anlotinib + chemotherapy showed the highest possibility to present the best PFS and OS versus chemotherapy. Compared with ICI plus chemotherapy, it also achieved significantly better PFS and presented a tendency of OS benefit. As for safety and toxicity, patients treated with benmelstobart + anlotinib + chemotherapy and durvalumab + tremelimumab + chemotherapy suffered a higher likelihood of more grade ≥ 3 AEs without unexpected AEs. Conclusion PD‐1/PD‐L1 inhibitors‐based combinations are associated with significant improvement in both PFS and OS for treatment‐naïve ES‐SCLC patients. Benmelstobart plus anlotinib with chemotherapy (CT) yielded better survival benefit versus CT alone or other ICIs + CT with caution for more adverse effects along with the addition of an antiangiogenic agent.

IntroductionIn recent years, some clinical studies of first-line treatment for advanced-stage urothelial carcinoma (aUC) have reached the main endpoint, showing inconsistent clinical efficacy. We hope to explore the efficacy and safety of first-line treatment for aUC.MethodsThe relevant literature from January 2000 to February 2024 was searched, and the R language (version 4.3.1) was used to perform a network meta-analysis based on the JAGS package and GEMTC package under the Bayesian framework. The main indicators included OS, PFS, ORR and adverse events of grade 3 or higher. This study has been registered in PROSPERO (CRD42024525372).ResultsA total of 8 RCTs involving 5539 patients and 12 treatments were included. Pembrolizumab plus Enfortumab Vedotin (PEM+EV) was significantly better than other groups in OS, PFS and ORR. In terms of OS, PEM+EV was significantly better than nivolumab plus platinum-based chemotherapy (NIVO+platinumCT) (HR=0.60; 95% CI: 0.45-0.81), PEM+platinumCT (HR=0.55; 95%CI: 0.42-0.72), atezolizumab (ATE) + platinumCT (HR=0.57; 95%CI: 0.43-0.75) and platinumCT (HR=0.47; 95%CI: 0.38-0.58). In terms of PFS, PEM+EV was also significantly better than NIVO+platinumCT (HR=0.62; 95%CI: 0.48-0.82), PEM+platinumCT (HR=0.58; 95%CI: 0.45-0.74), ATE+platinumCT (HR=0.55; 95%CI: 0.43-0.69) and platinumCT (HR=0.45; 95%CI: 0.38-0.54). In terms of ORR, PEM+EV had a significant be nefit compared with other treatment measures, which was 2.63 times that of platinumCT (OR=2.63; 95%CI: 2.00-3.45). The adverse events of grade 3 or higher in immunotherapy (ATE, PEM, durvalumab) was significantly lower than other treatment measures.ConclusionsPEM+EV can significantly prolong OS and PFS compared with other treatments, and has a higher ORR. The adverse events of grade 3 or higher of ATE was the lowest.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024525372, identifier CRD42024525372.

Abstract IgG4-related disease (IgG4-RD) has complex clinical manifestations ranging from fibrosis and inflammation to deregulated metabolism. The molecular mechanisms underpinning these phenotypes are unclear. In this study, by using IgG4-RD patient peripheral blood mononuclear cells (PBMCs), IgG4-RD cell lines and Usp25 knockout mice, we show that ubiquitin-specific protease 25 (USP25) engages in multiple pathways to regulate fibrotic and inflammatory pathways that are characteristic to IgG4-RD. Reduced USP25 expression in IgG4-RD leads to increased SMAD3 activation, which contributes to fibrosis and induces inflammation through the IL-1β inflammatory axis. Mechanistically, USP25 prevents ubiquitination of RAC1, thus, downregulation of USP25 leads to ubiquitination and degradation of RAC1. Decreased RAC1 levels result in reduced aldolase A release from the actin cytoskeleton, which then lowers glycolysis. The expression of LYN, a component of the B cell receptor signalosome is also reduced in USP25-deficient B cells, which might result in B cell activation deficiency. Altogether, our results indicate a potential anti-inflammatory and anti-fibrotic role for USP25 and make USP25 a promising diagnostic marker and potential therapeutic target in IgG4-RD.

Abstract Targeting tumor-infiltrating regulatory T cells (Tregs) is an efficient way to evoke an anti-tumor immune response. However, how Tregs maintain their fragility and stability remains largely unknown. IFITM3 and STAT1 are interferon-induced genes that play a positive role in the progression of tumors. Here, we showed that IFITM3-deficient Tregs blunted tumor growth by strengthening the tumor-killing response and displayed the Th1-like Treg phenotype with higher secretion of IFNγ. Mechanistically, depletion of IFITM3 enhances the translation and phosphorylation of STAT1. On the contrary, the decreased IFITM3 expression in STAT1-deficient Tregs indicates that STAT1 conversely regulates the expression of IFITM3 to form a feedback loop. Blocking the inflammatory cytokine IFNγ or directly depleting STAT1-IFITM3 axis phenocopies the restored suppressive function of tumor-infiltrating Tregs in the tumor model. Overall, our study demonstrates that the perturbation of tumor-infiltrating Tregs through the IFNγ-IFITM3-STAT1 feedback loop is essential for anti-tumor immunity and constitutes a targetable vulnerability of cancer immunotherapy.

Skin cancer is one of the most common types of cancer globally. Despite the remarkable advancements of deep learning methods in computer vision, automatic diagnosis of skin diseases still faces challenges such as limited data and class imbalance. Generative Adversarial Networks (GANs), which can synthesize realistic data, appear as an alternative to mitigate these issues. However, for imbalanced data, unconditional GANs either generate uneven data distribution or neglect universal knowledge of the whole dataset, while state-of-the-art (SOTA) conditional GANs suffer from performance degradation due to the mode collapse of minority classes. This paper proposes a two-stage GAN-based method to synthesize fine-grained and diverse $256 \times 256$ pixels skin lesion images for the imbalanced dataset, named Self-Transfer GAN (STGAN). STGAN first learns universal knowledge from all classes then transfers this shared knowledge to each class and fuses it with class-specific knowledge to synthesize high-quality images. Furthermore, based on STGAN, a framework to enhance the classification performance is established. Both data generation and classification tasks are evaluated on HAM10000 dataset. In terms of Fréchet Inception Distance (FID), Inception Score, Precision, and Recall, STGAN improved by 16%, 16%, 4%, 33% compared with SOTA conditional StyleGAN2. For classification, the STGAN-based framework achieves remarkable results, with an Accuracy of 98.23%, Sensitivity of 88.85%, Precision of 90.23%, F1-score of 89.48%, and Specificity of 98.34%.

Historically, white clover (Trifolium repens) seed was included in turfgrass seed mixtures to provide biodiversity and nitrogen (N) to lawns. White clover dicultures have been studied recently for inclusion in both warm- and cool-season turfgrasses, with the goals of reducing fertilizer applications and providing pollinator forage in lawns; however, other clovers have not been as widely researched in turfgrass. The objectives of this study were to evaluate 1) if white, strawberry (T. fragiferum), crimson (T. incarnatum), and rose (T. hirtum) clovers can persist in dicultures with Kentucky bluegrass (Poa pratensis); 2) if clover inclusion in dicultures impacts broadleaf weed cover; and 3) if low levels of N fertilization impact clover persistence or quality of clover–bluegrass dicultures. Kentucky bluegrass was grown as a monoculture or as a diculture with each of the four clover species. Each mono- or diculture was then treated with a low rate of N fertilizer (48.8 kg⋅ha–1 N) or no N fertilizer to determine quality and percentage of grass, clover, or weed and bare-soil cover. Dicultures contained similar or less weed and bare-soil cover, and maintained similar or greater quality compared with bluegrass monocultures, indicating clover and Kentucky bluegrass dicultures are suitable alternatives to Kentucky bluegrass monoculture lawns, and can potentially lead to reduced fertilizer and pesticide requirements. Fertilizer generally had no effect on cover, likely because of the low rates of N applied.

Abstract Purpose Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time‐consuming manual case‐finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real‐time monitoring of novel therapies.

High-fat diet (HFD) has long been recognized as risk factors for the development and progression of ulcerative colitis (UC), but the exact mechanism remained elusive. Here, HFD increased intestinal deoxycholic acid (DCA) levels, and DCA further exacerbated colonic inflammation. Transcriptome analysis revealed that DCA triggered ferroptosis pathway in colitis mice. Mechanistically, DCA upregulated hypoxia-inducible factor-2α (HIF-2α) and divalent metal transporter-1 (DMT1) expression, causing the ferrous ions accumulation and ferroptosis in intestinal epithelial cells, which was reversed by ferroptosis inhibitor ferrostatin-1. DCA failed to promote colitis and ferroptosis in intestine-specific HIF-2α-null mice. Notably, byak-angelicin inhibited DCA-induced pro-inflammatory and pro-ferroptotic effects through blocking the up-regulation of HIF-2α by DCA. Moreover, fat intake was positively correlated with disease activity in UC patients consuming HFD, with ferroptosis being more pronounced. Collectively, our findings demonstrated that HFD exacerbated colonic inflammation by promoting DCA-mediated ferroptosis, providing new insights into diet-related bile acid dysregulation in UC.

BackgroundNowadays, e-learning significantly affects college students’ academic life. This study aims to examine the factors that influence college students’ satisfaction with online learning outcomes.MethodThe study population consisted of undergraduate students from Dalian Medical University, with a total of 715 college students participating in the study. Out of these participants, 602 valid questionnaires were obtained. Demographic data was analyzed using SPSS.22, and the data was cleaned and prepared for testing the research hypotheses. The proposed research framework was examined using structural equation modeling (SEM) through Smart-PLS 3.0.ResultsThe results of the study showed that student satisfaction with learning outcomes was positively correlated with several factors: quality of teacher instruction (β = 0.100, p

Introduction: China has the world’s largest amount of antimony resources and plays a vital role in the global antimony industry. The Huangyangling area in Xinjiang belongs to the Lazhulong-Huangyangling antimony mineralization belt; this area is rich in antimony ore resources, with potential for discovery of additional ore. This high-elevation mountainous area is located in the northwestern region of the northern Tibetan Plateau, with a harsh natural environment and poor transportation access; thus, the use of traditional geological exploration methods is limited, and remote sensing technology provides unique advantages.Methods: Using Landsat 8 OLI and Gaofen-6 (GF-6) data, we extracted stratigraphic, lithologic and fault information from the study area through information enhancement processing of the images. Based on Landsat 8 OLI, Sentinel-2 and ASTER images and the use of remote sensing image preprocessing and interference information masking, the spectral characteristics of limonite, as well as hydroxyl, carbonate, and silicified alteration minerals were utilized; the characteristics included reflection or absorption in specific spectral bands. The spectral characteristics were then combined with principal component analysis (PCA) and the band ratio method to extract the alteration information for the study area.Results: The stratigraphy and boundaries between stratigraphic and lithologic units extracted in this study were defined, and numerous small faults were identified based on a comparison with 1:250,000 geologic maps. And the overall distribution trends of the indicators of alteration extracted in this study were located along fault zones; additionally, the hydroxyl and carbonate alteration anomalies matched well with known deposits (occurrences). Thus, the alteration information is a good indication of antimony ore bodies. Based on the geological interpretation, alteration information extraction and geochemical exploration data, four prospecting areas were identified.Discussion: The method of extracting geological and hydrothermal alteration information by remote sensing has practical significance because it provides the possibility for geological work and mineral exploration in difficult high-elevation environments.

Abstract It is challenging to characterize single or a few biomolecules in physiological milieus without excluding the influences of surrounding environment. Here we utilize optical plasmonic trapping to construct a dynamic nanocavity, which reduces the diffraction-limited detection volume and provides reproducible electromagnetic field enhancements to achieve high-throughput single-molecule surface-enhanced Raman spectroscopy (SERS) characterizations in aqueous environments. Specifically, we study human Islet Amyloid Polypeptide (amylin, hIAPP) under different physiological pH conditions by combining spectroscopic experiments and molecular dynamics (MD) simulations. Based on a statistically significant amount of time-dependent SERS spectra, two types of low-populated transient species of hIAPP containing either turn or β-sheet structure among its predominant helix-coil monomers are characterized during the early-stage incubation at neutral condition, which play a crucial role in driving irreversible amyloid fibril developments even after a subsequent adjustment of pH to continue the prolonged incubation at acidic condition. Our results might provide profound mechanistic insight into the pH-regulated amyloidogenesis and introduce an alternative approach for investigating complex biological processes at the single-molecule level.

Abstract Background As China's aging population continues to grow, the prevalence of mental illness among the seniors has been steadily increasing. The aim of this study is to reveal the changing trends and characteristics of economic burden among seniors patients with long-term hospitalization for mental illness, and to analyze the influencing factors. Methods The data for this study were gathered from seniors’ patients with mental illness who were hospitalized and aged 60 years or older. The patients were admitted to four specialized and general hospitals located in Dalian city between January 2018 and December 2020. The types of diseases include affective mental disorders (mood disorders), Schizophrenia, schizotypal, and delusional disorders, Organic (including symptomatic) mental disorders, Neurotic, stress-related and somatoform disorders, Mental retardation, Mental and behavioral disorders due to substance use. (Identify the main diagnosis at discharge using ICD-10 coding). This study analyzed the basic characteristics and disease-related information of seniors patients with long-term psychiatric disorders who were hospitalized, and explored the factors influencing hospitalization costs among patients with different illnesses. Results Among the 3871 study subjects, the average length of hospital stay was 127.51 days. The average hospitalization expenses per case were 33,656.07 yuan. Seniors’ patients with mental illness who receives treatment in specialized hospitals have higher hospitalization costs. Long-term hospitalization increases the total hospitalization costs. Age has an impact on hospitalization costs for patients with organic mental disorders. Patients with affective disorders (mood disorders) and neurotic, stress-related, and somatoform disorders who are covered by urban employee medical insurance have higher hospitalization costs.Patients with severe psychiatric disorders who have a 31-day readmission plan, as well as senior patients with somatoform disorders comorbid with other illnesses, incur higher hospitalization costs. Conclusions We should take corresponding measures to reduce the number of readmissions for patients with severe mental illnesses. The impact of treatment methods and differences in healthcare institutions on total hospitalization costs deserves further research. It is necessary to strengthen the prevention and diagnosis of comorbid physical illnesses in patients with mental disorders. The burden of mental illnesses in the seniors is significant, and medical insurance policies should be inclined towards providing support.

The 2008 melamine crisis led to widespread consumer distrust of Chinese domestic infant milk formula (IMF), which was substituted through a surge of imported products. Recent studies, however, suggest a revival in consumer confidence in Chinese domestic products and regulatory supervision. This coincides with a rise in consumer ethnocentrism and increased concern about foreign IMF, which arose during the COVID-19 pandemic. This study aims to investigate the multifaceted factors that influence consumers’ intention to purchase domestic IMF, using a structural equation model based on a survey of 563 online consumers. Our findings challenge conventional thinking that food safety governance, consumer ethnocentrism, and COVID-19 have no significant direct impact on IMF purchase decisions. Instead, consumer purchase intentions are driven more indirectly by mediating factors of perceived product quality and perceived behavioural control. The findings have significant implications for Chinese policymakers and industry stakeholders seeking to rebuild trust and strengthen the market position of domestic IMF in the post-melamine and post-pandemic era. By understanding the nuanced dynamics and consumer preferences in this market, international stakeholders can also develop more effective strategies to navigate and compete in the ever-evolving landscape of the Chinese IMF industry.

Thermal infrared imaging could detect hidden faults in various types of high-voltage power equipment, which is of great significance for power inspections. However, there are still certain issues with thermal-imaging-based abnormal heating detection methods due to varying appearances of abnormal regions and complex temperature interference from backgrounds. To solve these problems, a contour-based instance segmentation network is first proposed to utilize thermal (T) and visual (RGB) images, realizing high-accuracy segmentation against complex and changing environments. Specifically, modality-specific features are encoded via two-stream backbones and fused in spatial, channel, and frequency domains. In this way, modality differences are well handled, and effective complementary information is extracted for object detection and contour initialization. The transformer decoder is further utilized to explore the long-range relationships between contour points with background points, and to achieve the deformation of contour points. Then, the auto-encoder-based reconstruction network is developed to learn the distribution of power equipment using the proposed random argument strategy. Meanwhile, the UNet-like discriminative network directly explores the differences between the reconstructed and original image, capturing the deviation of poor reconstruction regions for abnormal heating detection. Many images are acquired in transformer substations with different weathers and day times to build the datasets with pixel-level annotation. Several extensive experiments are conducted for qualitative and quantitative evaluation, while the comparison results fully prove the effectiveness and robustness of the proposed instance segmentation method. The practicality and performance of the proposed abnormal heating detection method are evaluated on image patches with different kinds of insulators.

Aims: The aim of this study was to investigate the global and local impact of fat on bone in obesity by using the diet-induced obese (DIO) mouse model. Methods: In this study, we generated a diet-induced mouse model of obesity to conduct lipidomic and 3D imaging assessments of bone marrow fat, and evaluated the correlated bone adaptation indices and bone mechanical properties. Results: Our results indicated that bone mass was reduced and bone mechanical properties were impaired in DIO mice. Lipidomic sequencing and bioinformatic analysis identified 373 differential lipids, 176 of which were upregulated and 197 downregulated. Functional enrichment analysis revealed a significant downregulation of the pathways: fat digestion and absorption (ko04975) and lipolysis regulation in adipocytes (ko04923) in DIO mice, leading to local fat accumulation. The use of 3D imaging confirmed the increase in fat accumulation within the bone marrow cavity of obese mice. Conclusion: Our study sheds light on the intricate interplay between fat and bone, and provides a non-toxic and non-invasive method for measuring marrow adipose tissue. Cite this article: Bone Joint Res 2023;12(9):580–589.

Abstract Background Class attendance is important for academic performance. Personal interactions between teachers and students are difficult in large classes; the number of medical undergraduate students in China ranges from dozens to over 100. It is important for teachers to control the teaching process to improve student attendance and participation. Methods Two classes of fourth-year undergraduate medical students, with each class comprising 115 students, participated in the study. One class, the trial group, was taught by the block-based teaching method based on cybernetics. This study was conducted with three of the courses in the Introduction to Oncology subject, and the trial group’s courses included several blocks. Each block had a test paper that the students responded to immediately in class using the Internet. The teacher obtained feedback from the students when the rate of correct responses to block-test questions was less than 90%. The teacher adjusted the teaching in the following blocks according to the feedback information. The other class, the control group, was taught using the traditional lecture-based teaching method. Results The average attendance in the trial group was 104/115 (90.43%), and that in the control group was 83/115 (72.17%) (p = 0.0003). The teacher adjusted the teaching three times in the radiotherapy course owing to the complex ideas. After feedback, information on chemotherapy for the upper body was adjusted once, as was that on chemotherapy for the lower body, owing to students’ attitudes. The average total score of the trial group was 86.06 ± 17.46 and that of the control group was 80.38 ± 6.97 (p = 0.041). Questionnaire I showed that the trial group students’ attendance and participation were better than in the control group. Questionnaire II showed that the block-based teaching method based on cybernetics was approved by the students. Conclusions The block-based teaching method based on cybernetics used in medical classes with large numbers of Chinese undergraduate students had positive effects.

Abstract Background Hundreds of single‐nucleotide polymorphism (SNP) sites have been found to be potential genetic markers of type 2 diabetes mellitus (T2DM). However, SNPs related to T2DM in minipigs have been less reported. This study aimed to screen the T2DM‐susceptible candidate SNP loci in Bama minipigs so as to improve the success rate of the minipig T2DM model. Methods The genomic DNAs of three Bama minipigs with T2DM, six sibling low‐susceptibility minipigs with T2DM, and three normal control minipigs were compared by whole‐genome sequencing. The T2DM Bama minipig‐specific loci were obtained, and their functions were annotated. Meanwhile, the Biomart software was used to perform homology alignment with T2DM‐related loci obtained from the human genome‐wide association study to screen candidate SNP markers for T2DM in Bama miniature pigs. Results Whole‐genome resequencing detected 6960 specific loci in the minipigs with T2DM, and 13 loci corresponding to 9 diabetes‐related genes were selected. Further, a set of 122 specific loci in 69 orthologous genes of human T2DM candidate genes were obtained in the pigs. Collectively, a batch of T2DM‐susceptible candidate SNP markers in Bama minipigs, covering 16 genes and 135 loci, was established. Conclusions Whole‐genome sequencing and comparative genomics analysis of the orthologous genes in pigs that corresponded to the human T2DM‐related variant loci successfully screened out T2DM‐susceptible candidate markers in Bama miniature pigs. Using these loci to predict the susceptibility of the pigs before constructing an animal model of T2DM may help to establish an ideal animal model.

Abstract Background By analyzing 23 evaluation indicators included in 14 national basic public health service programs in a region of Hohhot City, Inner Mongolia Autonomous Region, China, the performance of basic public health services in the region in 2021 were analyzed to clarify the implementation and conduct of relevant programs. We also use this study as a basis to radiate the work of municipal basic public health services centered on the region and the outstanding problems reflected and to provide theoretical contents and suggestions that can be referred to for the same regions in central and western China as well as worldwide. Methods Using the TOPSIS method as the basis for the data analysis method, the evaluation indexes are ranked in terms of their proximity to the idealized target, and combined with the entropy value method, Technique for order preference by similarity to an ideal solution (TOPSIS) and rank-sum ratio (RSR) were used to rank 14 basic health care providers by grade. A comprehensive evaluation of the performance of basic public health services in a region of Hohhot City, Inner Mongolia Autonomous Region in 2021 was conducted through a joint model of entropy -weighted TOPSIS and RSR, making full use of the characteristics and advantages of the fuzzy joint, and conducting a comprehensive analysis from the perspective of the ratio weight and the method of graded calculation, making the study more distinguishable and measureable. Results In this study, for the regional basic public health services, a total of 23 evaluation indicators of basic public health service projects were included, among which the top three indicators with the weight of the entropy value method indicators were found to include the rate of Chinese medicine health management for the elderly, the rate of health management for the elderly, and the BCG vaccination rate after the analysis of the weight of the indicators; After the entropy-weighted TOPSIS evaluation showed that the Ci values of the regions were found to be between 0.378 and 0.715 through the calculation of the positive and negative ideal values of each indicator; RSR staging method evaluation showed that three community health centers (X2, X10, X12) had excellent evaluations of basic public health services; The number of evaluations as poor and moderate are 2 (X3, X9) and 9 (X1, X4, X5, X6, X7, X8, X11, X13, X14), respectively; Finally, the results of the entropy-weighted TOPSIS method and the fuzzy joint model of RSR staging method are basically consistent with the overall trend of the above two methods, and the reliability and credibility of the research results are high. Conclusion The entropy-weighted TOPSIS and RSR joint model can evaluate the effectiveness of basic public health services in a more comprehensive and holistic way. The results of the RSR staging results and the related weight ratio analysis show that the basic public health service programs in Hohhot, Inner Mongolia Autonomous Region are relatively balanced, but there are some differences; The same genus of elderly Chinese medicine health management rate, health management rate of the elderly, BCG vaccination rate several indicators accounted for a higher weight, its correlation with the key population-related items is high, suggesting that the future key population health service items should be focused on, and future research should be suggested from two key research.

Abstract Background The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. Methods Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. Results A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. Conclusions Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Fragility fractures, which are more prevalent in women, may be significantly influenced by autophagy due to altered bone turnover. As an essential mediator of autophagy, Beclin-1 modulates bone homeostasis by regulating osteoclast and chondrocyte differentiation, however, the alteration in the local bone mechanical environment in female Beclin-1+/− mice remains unclear. In this study, our aim is to investigate the biomechanical behavior of femurs from seven-month-old female wild-type (WT) and Beclin-1+/− mice under peak physiological load, using finite element analysis on micro-CT images. Micro-CT imaging analyses revealed femoral cortical thickening in Beclin-1+/− female mice compared to WT. Three-point bending test demonstrated a 63.94% increase in whole-bone strength and a 61.18% increase in stiffness for female Beclin-1+/− murine femurs, indicating improved biomechanical integrity. After conducting finite element analysis, Beclin-1+/− mice exhibited a 26.99% reduction in von Mises stress and a 31.62% reduction in maximum principal strain in the femoral midshaft, as well as a 36.64% decrease of von Mises stress in the distal femurs, compared to WT mice. Subsequently, the strength-safety factor was determined using an empirical formula, revealing that Beclin-1+/− mice exhibited significantly higher minimum safety factors in both the midshaft and distal regions compared to WT mice. In summary, considering the increased response of bone adaptation to mechanical loading in female Beclin-1+/− mice, our findings indicate that increasing cortical bone thickness significantly improves bone biomechanical behavior by effectively reducing stress and strain within the femoral shaft.

Abstract As a highly selective, recoverable, and low‐cost process, macroporous resins (MPRs) are of interest for the purification of bioactive components from natural products. This guide takes super‐crosslinked MPR as an example, and focuses on application of macroporous adsorption resin in polysaccharide purification, including pretreatment and regeneration of macroporous adsorption resin, various factors affecting the adsorption effect of macroporous adsorption resin, and adsorption mechanism of resin.

Maple syrup is a well-known natural sweetener made from the sap harvested from maple trees (Acer sp.). The North American scientific literature regarding maple syrup has predominantly originated in the Northeastern United States and Canada. However, the range of this Holarctic genus extends across the continent and all species produce sap with the potential for syrup production. This study focuses on two maple species commonly found in Northern Utah, namely the native boxelder (Acer negundo) and the introduced Norway maple (Acer platanoides). Thirty trees of each species were tapped in Cache Valley, UT, USA, on 19 Feb 2022, and measured for daily sap yield and sugar content until the season ended 37 days later on 27 Mar 2022. The same trees were re-tapped on 1 Mar 2023 and taps were removed 41 days later on 10 Apr 2023. Average 2022 sap yields were 22.1 L for boxelder and 7.5 L for Norway maple per tree. In 2023, average sap yields were 26.4 L for boxelder and 9.3 L for Norway maple per tree. Boxelder trees produced an average sap yield more than double that of Norway maple in both years. Sugar content was similar for both species ranging from 2.2% to 2.8%. Air temperatures were analyzed using data from Utah AgWeather System weather stations nearest to the trees, and air temperature had a significant impact on sap yield. It was found that an average daily air temperature of 0.5 °C and a daily air temperature difference of ∼10 °C with a minimum air temperature close to −5 °C and a maximum air temperature of ∼6 °C was the optimal condition for production. An analysis of the mineral nutrient concentrations in the sap and soil showed no correlation. These findings indicate that there is potential for using Utah’s maple species for syrup production.

Food waste is a common issue arising from grinding of food by experimental animals, leading to excessive food scraps falling into cages. In the wild, animals grind food by gnawing vegetation and seeds, potentially damaging the ecological environment. However, limited ecology studies have focused on food grinding behavior since the last century, with even fewer on rodent food grinding, particularly recently. Although food grinding’s function is partially understood, its biological purposes remain under-investigated and driving factors unclear. This review aims to explain potential causes of animal food grinding, identify influencing factors, and discuss contexts and limitations. Specifically, we emphasize recent progress on gut microbiota significance for food grinding. Moreover, we show abnormal food grinding is determined by degree of excess normal behavior, emphasizing food grinding is not meaningless. Findings from this review promote comprehensive research on the myriad factors, multifaceted roles, and intricate evolution underlying food grinding behavior, benefiting laboratory animal husbandry and ecological environment protection, and identifying potential physiological benefits yet undiscovered.

A series of novel amine triphenolate iron complexes were synthesized and characterized using UV, IR, elemental analysis, and high-resolution mass spectrometry. These complexes were applied to the ring-opening polymerization (ROP) of cyclohexene oxide (CHO), demonstrating excellent activity (TOF > 11050 h−1) in the absence of a co-catalyst. In addition, complex C1 maintained the dimer in the presence of the reaction substrate CHO, catalyzing the ring-opening polymerization of CHO to PCHO through bimetallic synergy. Furthermore, a two-component system consisting of iron complexes and TBAB displayed the ability to catalyze the reaction of CHO with CO2, resulting in the formation of cis-cyclic carbonate with high selectivity. Complex C4 exhibited the highest catalytic activity, achieving 80% conversion of CHO at a CHO/C4/TBAB molar ratio of 2000/1/8 and a CO2 pressure of 3 MPa for 16 h at 100 °C, while maintaining >99% selectivity of cis-cyclic carbonates, which demonstrated good conversion and selectivity.

This paper explores the paradigm of leveraging ChatGPT as a benchmark tool for theory prototyping in conceptual research. Specifically, we conducted two experimental studies using the classical technology acceptance model (TAM) to demonstrate and evaluate ChatGPT's capability of comprehending theoretical concepts, discriminating between constructs, and generating meaningful responses. Results of the two studies indicate that ChatGPT can generate responses aligned with the TAM theory and constructs. Key metrics including the factors loading, internal consistency reliability, and convergence reliability of the measurement model surpass the minimum threshold, thus confirming the validity of TAM constructs. Moreover, supported hypotheses provide an evidence for the nomological validity of TAM constructs. However, both of the two studies show a high Heterotrait–Monotrait ratio of correlations (HTMT) among TAM constructs, suggesting a concern about discriminant validity. Furthermore, high duplicated response rates were identified and potential biases regarding gender, usage experiences, perceived usefulness, and behavioural intention were revealed in ChatGPT-generated samples. Therefore, it calls for additional efforts in LLM to address performance metrics related to duplicated responses, the strength of discriminant validity, the impact of prompt design, and the generalizability of findings across contexts.

ObjectiveWe aimed to assess the association between weight-adjusted waist circumference index (WWI) and gallstone prevalence in US adults.MethodsWe selected individuals from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2020 and used logistic regression analyses, subgroup analyses, and dose-response curves to assess the association between WWI and gallbladder stone prevalence and age, sex, and ethnicity.ResultsA total of 7971 participants aged ≥20 years were enrolled in our study; 828 patients had a self-reported history of gallstones. After correcting for confounders, for each unit of WWI after Ln conversion, the prevalence of gallbladder stones increased by 34% (OR=1.34, 95% CI:1.20, 1.50). Dose-response curves showed a positive correlation between WWI and gallbladder stone prevalence.According to the subgroup analysis, the positive association between TyG index and high-frequency HI was more significant in males(OR=1.34, 95% CI:1.07, 1.69),

Paeonia lactiflora is a high-value crop with a temperature-dependent growth response that requires worldwide production to satisfy year-round demand. The objective of this study was to evaluate production and timing of ‘Coral Charm’ peony as a cool-season crop in the US Intermountain West. High-tunnel and field production were trialed in North Logan, UT, USA (lat. 41.77°N, long. 111.81°W; elevation, 1382 m) with the addition of low tunnels and soil heating methods to advance growth in 2019–21. Soil and air temperatures, as well as the date and quality of harvested stems, were measured. High tunnels yielded 15.7 ± 3.3 to 19.4 ± 2.1 stems/m2 [± standard error (SE)] and the high tunnel alone advanced initial harvest 21 to 34 days earlier than natural field conditions. The field yielded 16.1 ± 1.9 to 20.8 ± 1.6 stems/m2 and staggered production, resulting in a harvest duration up to 38 days across the high tunnel and field. The use of a low tunnel with soil heating advanced the initial harvest date compared with natural (i.e., unmanipulated) high-tunnel and field conditions by 3 and 7 days in 2019, 6 days in 2020, and 16 and 6 days in 2021 in the high tunnel and field, respectively. However, the quality decreased significantly under low tunnels with soil heating within high tunnels, compared with unheated plants, as a result of superoptimal temperatures and humidity that damaged buds and led to an increase in disease and insect pressure. Overall, increasing soil temperature advanced early stages of production when the meristem was below or near the soil surface, whereas increased air temperatures accelerated stem elongation and advanced time to flowering.

Immune checkpoint inhibitors and antiangiogenic agents have shown some activity in patients with late-stage gastric cancer. Here the authors report the results of a phase II trial of neoadjuvant anti-PD1 (camrelizumab), antiangiogenic agent (apatinib), and chemotherapy (S-1 ± Oxaliplatin) in stage T4a/bN + M0 gastric cancer patients.

The microstructure and corrosion property of super 13Cr martensitic stainless steel (S13CrMSS) processed by quenching, one-step tempering and two-step tempering were investigated by XRD, SEM, EBSD and TEM-EDS analysis. It has been found that volume fraction of the reversed austenite in the sample treated by two-step tempering treatment was 13.5 vol. % up to the maximum. The reversed austenite almost always nucleated and distributed in martensite laths boundary during tempering and the diffusion of Ni, Mn and C promoted its growth. Enrichment of such elements as Ni, Mn, C and the ultrafine size of the reversed austenite were considered to be the main factor that accounted for the stability of the reversed austenite. At the same time, martensite and reversed austenite satisfy the (1 1¯ 1)γ // (011)α' and [011]γ // [1¯1¯ 1]α' Kurdjumov-Sachs (K–S) orientation relationship. Potentiodynamic polarization curves and electrochemical impedance spectroscopy indicated that two-step tempered specimen exhibited a higher pitting potential, lower corrosion current density, thicker passivation film and superior pitting resistance than the quenching specimen (without reversed austenite) as well as one-step tempering specimen (3.6 vol.%). It has been confirmed that the reversed austenite has a beneficial effect on the corrosion property of S13CrMSS.

Abstract In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics $$C_{\text {td}}$$ C td over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved $$C_{\text {td}}$$ C td 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained $$C_{\text {td}}$$ C td 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

ObjectiveSwallowing examination is crucial in patients with dysphagia. We aimed to compare qualitative and quantitative videofluoroscopic swallowing study (VFSS) results to provide reference for standardizing quantitative parameters.Materials and methodsIn total, 117 patients with dysphagia were included, 38 with Parkinson’s disease and 39 and 40 in convalescence following cerebral hemorrhage and infarction. VFSS was both qualitatively and quantitatively analyzed.ResultsA significant difference of Oral transit time was found between the oral motor function grades (p < 0.001), also was swallowing reaction times found between swallowing reaction duration grades (p < 0.001), and soft palate lift duration between the soft palate lift grades (p < 0.001). Superior hyoid bone movement (p < 0.001), anterior hyoid bone movement (p < 0.001), hyoid pause time (p < 0.001), and hyoid movement duration (p = 0.032) had significant differences between the hyoid laryngeal complex movement grades, as did the pharyngeal cavity transit time among the cricopharyngeal muscle opening duration grades (p < 0.001). The laryngeal vestibule closure duration differed among the glottic closure grades (p < 0.001). No statistically significant difference in upper esophageal sphincter opening diameter (p = 0.682) or duration (p = 0.682) among the cyclopharyngeal muscle opening duration grades. The pharyngeal area at rest did not significantly differ among the different vallecular residue (p = 0.202) and pyriform sinus residue (p = 0.116) grades.ConclusionSeveral quantitative parameters can reflect the swallowing assessment process well. Further optimization of quantitative parameters is recommended.

Abstract Background Oral immunotherapy (OIT) is a promising treatment for food allergies; however, safety is a concern. We synthesized evidence from the best randomized controlled trials (RCTs) on efficacy/safety of OIT for desensitization (DS) and remission (sustained unresponsiveness (SU)) in IgE mediated allergy to peanut, hen's eggs, and cow's milk. Body We searched Pubmed, EMBASE, and Cochrane databases (Until Oct 22) identifying 16 eligible RCTs published in English measuring food allergy by food challenge at the beginning and at the end of the study. The Cochrane Risk of Bias tool was used to assess study quality. We found 18 eligible studies. There was evidence of efficacy for DS for all allergens: peanut (RR 11.32; 95% CI 5.93, 21.60, I2 49%, 8 studies); hen's egg (RR 4.67; 2.66, 8.21, I2 0%, 5 studies); cow's milk (RR 13.98; 3.51, 55.65, I2 0%, 4 studies) and evidence for SU for peanut (RR 7.74; 2.90, 20.69, I2 0%, 3 studies) and hen's egg (RR 6.91; 1.67, 28.57, I2 0%, 2 studies). Allergic events were increased with intervention, and risk of adrenaline use increased for peanut RR 2.96; 1.63, 5.35, I2 0%, 8 studies; egg RR 1.71; 0.42, 6.92, I2 0%, 6 studies; and milk RR 8.45; 2.02, 35.27, I2 0%, 4 studies. Conclusion We found strong evidence that peanut, hen's egg, and cow's milk OIT can induce DS and some evidence for remission. There was a high risk of allergic reactions. Generalizability to the entire food allergic population is not known.

Micro-Doppler time–frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time–frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time–frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time–frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition.

The effects of different storage temperatures on the nutritional quality, color, and antioxidant capacity of lotus seed juice and the correlations between various physicochemical indices and antioxidant capacity during storage were investigated in this study. The results showed that the overall retention rate of various nutrients and antioxidant activity in lotus seed juice under low-temperature storage was better than that under 37 °C storage. Meanwhile, temperature had a significant effect on increasing the browning of lotus seed juice and the change in L*. The results of Pearson correlation and redundancy analysis (RDA) showed that the reduction in antioxidant activity in lotus seed juice aggravated the browning index of the system at high temperatures. The color changes in the system were closely related to the clarity of lotus seed juice and aging of starch at low temperatures.

Membraneless organelles play vital cellular roles, and control over their formation and state could have varied applications. Here, the authors develop photoresponsive synthetic condensates whose activity can be controlled through the use of light to trigger liquid-to-solid phase transition.

Abstract Traditional humidity sensors for respiration monitoring applications have faced technical challenges, including low sensitivity, long recovery times, high parasitic capacitance and uncalibrated temperature drift. To overcome these problems, we present a triple-layer humidity sensor that comprises a nanoforest-based sensing capacitor, a thermistor, a microheater and a reference capacitor. When compared with traditional polyimide-based humidity sensors, this novel device has a sensitivity that is improved significantly by 8 times within a relative humidity range of 40–90%. Additionally, the integration of the microheater into the sensor can help to reduce its recovery time to 5 s. The use of the reference capacitor helps to eliminate parasitic capacitance, and the thermistor helps the sensor obtain a higher accuracy. These unique design aspects cause the sensor to have an excellent humidity sensing performance in respiration monitoring applications. Furthermore, through the adoption of machine learning algorithms, the sensor can distinguish different respiration states with an accuracy of 94%. Therefore, this humidity sensor design is expected to be used widely in both consumer electronics and intelligent medical instrument applications.

This study aimed to investigate the effects of the covalent binding of flaxseed protein (FP) and chlorogenic acid (CA) on the structure and functional properties of FP–CA complexes fabricated using the alkali method. The results suggested that the encapsulation efficiency of CA encapsulated by FP ranged from 66.11% to 72.20% and the loading capacity of CA increased with an increasing addition ratio of CA with a dose-dependent relationship, which increased from 2.34% to 10.19%. The particle size, turbidity, zeta potential and PDI of FP and the FP–CA complexes had no significant discrepancy. UV–Vis and fluorescence spectra showed the existence of the interaction between FP and CA. SEM images showed that the surface of the FP–0.35%CA complex had more wrinkles compared to FP. Differential scanning calorimetry analysis indicated the decomposition temperature of FP at 198 °C was higher than that (197 °C) of the FP–0.35%CA complex, implying that the stability of the FP–CA complexes was lower than FP. The functional properties suggested that the FP–CA complex with 1.40% CA had a higher water holding capacity (500.81%), lower oil holding capacity (273.495%) and lower surface hydrophobicity. Moreover, the FP–CA complexes had better antioxidant activities than that of FP. Therefore, this study provides more insights for the potential application of FP–CA covalent complexes in functional food processing.

Low-carbon travel is an important part of low-carbon cities and low-carbon transportation, and low-carbon transportation is an inevitable choice to slow down the growth of carbon emissions in China. All countries in the world are actively promoting new energy vehicles and attach great importance to the application of the new energy industry in urban transportation. Commuting is an important part of urban life, and the choice of travel behavior has an important impact on traffic and environmental protection. Taking the Xi’an metropolitan area as an example, this paper expounds on the integrated development path of the industrial chain of new energy + travel in the metropolitan area and clarifies the energy transformation model of the integrated development of low-carbon transportation and energy. From the perspective of green and low-carbon, 1000 commuters were interviewed using a questionnaire survey, and the cumulative prospect model was used to verify the internal mechanism affecting commuters in metropolitan areas to choose new energy commuting. The results of the study show that new energy transportation modes play an important role in the low-carbon economy, and under different scenarios and assumptions, there are significant differences in the cumulative prospect values of the subway, new energy buses and fuel private cars, and corresponding optimization measures are proposed to increase the proportion of new energy commuting trips. The results will help further promote the development of a low-carbon economy and energy integration in the field of transportation and provide a reference for the sustainable development of public transportation.

Food grinding is supposed to be influenced by multiple factors. However, how those factors affecting this behavior remain unclear. In this study, we investigated the effect of food restriction on food grinding in Brandt’s voles (Lasiopodomys brandtii), as well as the potential role of the gut microbiota in this process, through a comparison of the variations between voles with different food supplies. Food restriction reduced the relative amount of ground food to a greater extent than it lowered the relative food consumption, and altered the abundance of Staphylococcus, Aerococcus, Jeotgalicoccus, and Un--s-Clostridiaceae bacterium GM1. Fecal acetate content for the 7.5 g-food supply group was lower than that for the 15 g-food supply group. Our study indicated that food restriction could effectively inhibit food grinding. Further, Un--s-Clostridiaceae bacterium GM1 abundance, Aerococcus abundance, and acetate content were strongly related to food grinding. Variations in gut microbial abundance and short-chain fatty acid content induced by food restriction likely promote the inhibition of food grinding. These results could potentially provide guidance for reducing food waste during laboratory rodent maintenance.

To investigate the hysteretic behavior of an X-shaped metal damper (XMD) with an oblique angle, cyclic loading tests were carried out on nine specimens, including two XMDs without buckling-restrained devices, four XMDs with stiffening ribs, and three XMDs with cover plates as references. The test results showed that the oblique angle could effectively increase the stiffness, strength, and energy dissipation of the XMD. When the oblique angle of an XMD with stiffening ribs increased from 0° to 30° at the applied displacement of 8.4 mm, the mean strengths and cumulative energy dissipation of specimens increased by about 80.77% and 80.57%, respectively. Although asymmetric hysteretic loops were also observed in specimens with an oblique angle and buckling-restrained devices, stable hysteretic curves were obtained. This indicated that the stiffening ribs and cover plates can effectively constrain the buckling behavior of XMDs. Additionally, the mean strengths of specimens with stiffening ribs were a little higher than those of specimens with cover plates. Subsequently, the finite element analysis models of the XMDs were proposed, in which the metal plasticity model considering isotropic and kinematical hardening was used to model the material properties of steel, and the simulation results matched well with the test results. Finally, the theoretical calculation method was proposed to predict the elastic stiffness of specimens, and the theoretical elastic stiffness matched well with the test results.

Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)–variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research.